Domain Generalization (DG): Existing methods tackle the problem of domain shift, which occurs when the training and testing data belong to different domains, in different ways. Most previous approaches aim to learn domain-invariance by minimizing the discrepancy between multiple source domains[1]}, [2]}, [3]} or by employing autoencoders and adversarial losses [4]}, [5]}. A few works [6]}, [7]}, [8]} introduce specific training policies or optimization procedures such as meta-learning and episodic training to enhance the generalizability of the model to unseen domains. Similarly, [9]}, [10]} employ data augmentation strategies to improve the models robustness to data distribution shifts at test time. However, all these works tackle the DG problem alone, where the label spaces at both train and test time are identical.
